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Development of statistical and computational methods for the analysis of graphs with applications in biological networks

Grant number: 12/25417-9
Support type:Scholarships in Brazil - Master
Effective date (Start): May 01, 2013
Effective date (End): April 30, 2015
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal Investigator:André Fujita
Grantee:Suzana de Siqueira Santos
Home Institution: Instituto de Matemática e Estatística (IME). Universidade de São Paulo (USP). São Paulo , SP, Brazil

Abstract

The brain's structural and functional systems, protein-protein interaction, and gene networks are just some examples of biological systems that share some features of complex networks, such as highly connected nodes, modularity, and small-world topology. Recent studies indicate that some pathologies present topological network alterations relative to the general population. Therefore, methods to discriminate the processes that generate the different classes of networks (for example, normal and disease) might be crucial for the diagnosis, prognosis, and treatment of the disease. It is known that several topological properties of a network (graph) can be described by the distribution of the spectrum (set of eigenvalues of the adjacency matrix and their multiplicity). Moreover, large networks generated by the same random process have the same spectrum distribution, which indicates that it can be used as a "fingerprint". Based on the concepts of the entropy of a random graph spectrum and of the Kullback-Leibler and Jensen-Shannon divergences between graphs spectra, we propose to develop statistical and computational methods for the analysis of random graphs (methods to discriminate graphs generated by different processes), as well to apply them in real biological data from Molecular Biology, in order to contribute not only to the integration of the Graph Theory and Statistics, but also to the elucidation of the biological mechanisms that generate the diseases.

Scientific publications
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
SANTOS, SUZANA DE SIQUEIRA; DE ALMEIDA GALATRO, THAIS FERNANDA; WATANABE, RODRIGO AKIRA; OBA-SHINJO, SUELI MIEKO; NAGAHASHI MARIE, SUELY KAZUE; FUJITA, ANDRE. CoGA: An R Package to Identify Differentially Co-Expressed Gene Sets by Analyzing the Graph Spectra. PLoS One, v. 10, n. 8 AUG 27 2015. Web of Science Citations: 9.
SANTOS, SUZANA DE SIQUEIRA; TAKAHASHI, DANIEL YASUMASA; NAKATA, ASUKA; FUJITA, ANDRE. A comparative study of statistical methods used to identify dependencies between gene expression signals. BRIEFINGS IN BIOINFORMATICS, v. 15, n. 6, p. 906-918, NOV 2014. Web of Science Citations: 32.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.